Intercomparison Of PurpleAir Sensor Performance Over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Wallace, Lance

doi:10.20944/preprints202202.0142.v1

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Due to absence of reference instruments, we opted to use a transparent and reproducible alternative method ALT CF3 of calculating PM 2.5 from the number of particles in three size categories. Note that this approach was applied successfully in a couple of studies [17] [18]. Basically, the PM concentrations data collected by PurpleAir low-cost sensors is corrected using the particle concentrations in three size bins as expressed in Equation ( 1):…”

Section: Data Correctionmentioning

confidence: 99%

Modeling and Characterization of Fine Particulate Matter Dynamics in Bujumbura Using Low-Cost Sensors

Ndamuzi,

Akimana,

Gahungu

et al. 2024

JAMP

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Air pollution is a result of multiple sources including both natural and anthropogenic activities. The rapid urbanization of the cities such as Bujumbura, economic capital of Burundi, is one of these factors. The very first characterization of the spatio-temporal variability of PM 2.5 in Bujumbura and the forecasting of PM 2.5 concentration have been conducted in this paper using data collected during a year, from August 2022 to August 2023, by low-cost sensors installed in Bujumbura city. For each commune, an hourly, daily and seasonal analysis was carried out and the results showed that the mass concentrations of PM 2.5 in the three municipalities differ from one commune to another. The average hourly and annual PM 2.5 concentrations exceed the World Health Organization standards. The range is between 28.3 and 35.0 μg/m 3 . In order to make a prediction of PM 2.5 concentration, an investigation of Recurrent Neural Networks with Long Short-Term Memory has been undertaken.

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Section: Data Correctionmentioning

confidence: 99%

Modeling and Characterization of Fine Particulate Matter Dynamics in Bujumbura Using Low-Cost Sensors

Ndamuzi,

Akimana,

Gahungu

et al. 2024

JAMP

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Testing a New “Decrypted” Algorithm for Plantower Sensors Measuring PM2.5: Comparison with an Alternative Algorithm

Wallace

2023

Algorithms

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Recently, a hypothesis providing a detailed equation for the Plantower CF_1 algorithm for PM2.5 has been published. The hypothesis was originally validated using eight independent Plantower sensors in four PurpleAir PA-II monitors providing PM2.5 estimates from a single site in 2020. If true, the hypothesis makes important predictions regarding PM2.5 measurements using CF_1. Therefore, we test the hypothesis using 18 Plantower sensors from four datasets from two sites in later years (2021–2023). The four general models from these datasets agreed to within 10% with the original model. A competing algorithm known as “pm2.5 alt” has been published and is freely available on the PurpleAir API site. The accuracy, precision, and limit of detection for the two algorithms are compared. The CF_1 algorithm overestimates PM2.5 by about 60–70% compared to two calibrated PurpleAir monitors using the pm2.5 alt algorithm. A requirement that the two sensors in a single monitor agree to within 20% was met by 85–99% of the data using the pm2.5 alt algorithm, but by only 22–74% of the data using the CF_1 algorithm. The limit of detection (LOD) of the CF_1 algorithm was about 10 times the LOD of the pm2.5 alt algorithm, resulting in 71% of the CF_1 data falling below the LOD, compared to 1 % for the pm2.5 alt algorithm.

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A Smoke Chamber Study on Some Low-Cost Sensors for Monitoring Size-Segregated Aerosol and Microclimatic Parameters

Bencs,

Nagy

2024

Atmosphere

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Low-cost sensors (LCSs) of Geekcreit PM1/PM2.5/PM10 (based on a PMS5003 sampler) and BOHU BH-1 models A3 and B3 (based on a Pando G7 sampler) were compared for different aerosol size ranges using a research-grade instrument (Grimm 1.109) under controlled laboratory conditions. An aerosol generator was utilized to produce various sizes of monodispersed particulate matter (PM), which was introduced into a laboratory smoke chamber under resistance heating/cooling and/or varying RH conditions. In addition, the accuracy of the air temperature (T) and relative humidity (RH) sensors of the LCSs were assessed against calibrated, laboratory-grade instruments. The study LCSs showed generally accurate readings for PM2.5, irrespectively of the slow T and/or RH changes, which provided apt conditions for accurate calibration slopes (S) and low intercepts/bias (b) of the linear fits. On the other hand, PM1 and PM10 readings slightly deviated from those observed with the reference monitor, likely due to the lower detection efficacy of the LCSs towards fine and coarse PM. Varying RH influenced the S and b values, showing its impact on the detection efficacy of LCSs. Under low/medium RH, homoscedastic calibration curves of PMx were found, whereas rather heteroscedastic calibration plots were observed at high RH. For T calibration, low RH in the smoke chamber provided more reproducible conditions in terms of lower measurement bias for LCSs as recorded against a calibrated, reference-grade thermometer.

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Intercomparison Of PurpleAir Sensor Performance Over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Cited by 7 publications

References 12 publications

Modeling and Characterization of Fine Particulate Matter Dynamics in Bujumbura Using Low-Cost Sensors

Modeling and Characterization of Fine Particulate Matter Dynamics in Bujumbura Using Low-Cost Sensors

Testing a New “Decrypted” Algorithm for Plantower Sensors Measuring PM2.5: Comparison with an Alternative Algorithm

A Smoke Chamber Study on Some Low-Cost Sensors for Monitoring Size-Segregated Aerosol and Microclimatic Parameters

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